Tillage methods and microbial fertilizers synergistically enhance soil organic carbon concentration in albic soil

Zhenlin Zhao; Song Cheng; Xiaolin Li; Chang Zhang; Ximing Liu; Jinyao Yan; Jingchao Yuan; Jianzhao Liu; Yao Liang; Wei Fan; Hongguang Cai; Zhenlin Zhao; Song Cheng; Xiaolin Li; Chang Zhang; Ximing Liu; Jinyao Yan; Jingchao Yuan; Jianzhao Liu; Yao Liang; Wei Fan; Hongguang Cai

doi:10.48130/aee-0026-0013

Albic soils often exhibit high bulk density and low organic matter content, which constrains soil organic carbon (SOC) accumulation and aggregate development. A one-year field experiment (2023–2024) in Shulan, Jilin Province (China) evaluated how tillage intensity and microbial organic fertilizer rate jointly regulate SOC, aggregate size stability, and carbon-cycling extracellular enzymes across two depths (0–20 and 20–40 cm). Three tillage practices—no-tillage (NT), plough tillage (PT), and rotary tillage (RT)—were combined with three microbial organic fertilizer rates (600, 1,200, and 2,400 kg ha⁻¹). NT without straw return served as the control. The tillage methods with microbial organic fertilizer interactions improved soil physicochemical conditions (lower bulk density and higher porosity and nutrient status) with depth-dependent responses. Under NT, the high microbial organic fertilizer rate increased SOC by 22% in the topsoil and by 93% in the subsoil relative to the control, indicating that reduced disturbance coupled with high rates of microbial organic inputs can promote SOC accumulation throughout 0–40 cm. Aggregate responses were treatment-specific: NT generally maintained a higher proportion and stability of macroaggregates, whereas PT tended to increase subsoil SOC but reduce macroaggregate stability, suggesting a trade-off between deep SOC gains and aggregate-based physical protection. Enzyme activities (AG, BG, CBH, and XYL) and correlation networks indicated that the dominant associations varied with depth. Topsoil SOC showed closer links with microbial indicators and aggregate properties, while subsoil SOC was more related to physicochemical constraints. Overall, conservation tillage (especially NT) combined with adequate microbial organic fertilizer inputs shows short-term potential to enhance SOC in Albic soils, while longer-term monitoring is needed to assess persistence and mechanisms.

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Introduction

Soil organic carbon (SOC) stabilization is a key pathway for mitigating climate change, as it enhances soil fertility and promotes soil improvement methods^[1−3]. As a vital component of soil, SOC performs multiple functions: regulating soil structure, facilitating nutrient cycling, enhancing water retention capacity, and ultimately increasing soil productivity. However, significant variations exist in carbon storage potential across different soil types (e.g., albic soils ≈ 9.2 g SOC kg⁻¹, ~ 35% below national mean; China Soil Survey, 2020), primarily due to their inherent physicochemical properties. Albic soils exhibit severely impaired aerobic decomposition of organic matter due to their compact structure and restricted aeration. This physiological constraint substantially reduces the rate of soil organic carbon accumulation. According to the China Soil Survey, 2020 (Bulletin No. 2020-05) and Lu et al.^[4], albic soil covers 26.7 million hm², accounting for 14.6% of the province's total soil area. Cultivated land of this soil type spans 50.3 hm², representing 9.4% of the province's total cultivated land area. Albic soil is the third dominant soil type in the region, highlighting its significance in regional soil composition. Consequently, there is an urgent need to develop and implement specialized management strategies specifically tailored to enhance carbon concentration efficiency in albic soils.

Intensive tillage and chemical fertilization often accelerate SOC mineralization and disrupt soil aggregates, thereby reducing long-term carbon storage^[5,6]. In contrast, conservation tillage methods, such as no tillage practice, minimize soil disturbance, preserve soil moisture, and promote the formation of macro-aggregates (> 0.25 mm)^[5]. These macro-aggregates physically protect organic matter from microbial decomposition, significantly lowering SOC mineralization rates. Deep plough tillage has the potential to significantly disrupt the existing soil structure. This disruption can exert adverse effects on the soil environment. In the short term, it may accelerate the decomposition of organic carbon^[7]. However, when deep plough tillage is combined with straw mulching, it can bring about positive changes in certain soil types. In albic soils, this combination can effectively break through the plow pan. The plow pan is a compacted layer that restricts root growth and water movement^[8]. By breaking it, the combination improves soil aeration, which is beneficial for soil organisms and plant roots^[9]. Nevertheless, when applying this combined approach, it is necessary to add organic materials. This is because the process may raise the risk of increased mineralization. The addition of organic materials helps to balance this risk and maintain a favorable carbon balance in the soil^[10]. Rotary tillage can lead to a short-term increase in soil carbon content, particularly when crop residues are returned to the field^[11]. However, prolonged application of rotary tillage tends to accelerate the mineralization and decomposition of soil organic carbon^[12]. This is especially detrimental to the stability of deep soil aggregates, which are crucial for maintaining overall soil structure and carbon concentration capacity. Furthermore, the repeated use of rotary tillage contributes to the formation of shallow plow layers and the development of a plow pan. These changes restrict root penetration and water movement, ultimately inhibiting the long-term accumulation and concentration of soil carbon.

Microbial fertilizers, comprising Bacillus subtilis, Bacillus amyloliquefaciens, and Trichoderma harzianum Rifai, critically regulate microbial metabolic processes that drive SOC concentration^[13−18]. For instance, the combined application of conservation tillage (no-tillage) and microbial fertilizers elevates key enzyme activities^[19], such as β-glucosidase (by 63.1%) and polyphenol oxidase, which facilitate lignin-mineral binding and promote the formation of stable organic carbon complexes. This synergy also optimizes microbial carbon-nitrogen stoichiometry. By reducing the β-glucosidase/peroxidase (BG/PER) ratio, the Combined Microbial and Straw (CMS) treatment alleviates microbial C–N imbalance, thereby suppressing co-metabolic mineralization of SOC. Long-term field investigations provide compelling evidence supporting the effectiveness of these integrated agricultural methods. Specifically, in albic soils, the application of CMS treatments significantly elevated Soil Organic Carbon (SOC) levels to 17.0 g kg⁻¹. This represents a substantial 28.3% increase relative to the conventional fertilization method (chemical fertilizer only, without straw mulching), and enhanced the proportion of carbon stored in macro-aggregate-pathway carbon physical (aggregate protection) and biological (microbial metabolic modulation) pathways to stabilize SOC. Nevertheless, the persistence of these benefits over decadal scales remains underexplored, highlighting a key research gap. The specificity of albic soils demands tailored solutions. Their low porosity and high acidity inhibit organic matter decomposition and microbial activity. Strategies such as deep plough tillage can mitigate compaction by breaking up hardened subsoil layers, while humus-enriched microbial fertilizers enhance carbon fixation under acidic conditions. Additionally, lignin-degrading microbial inoculants paired with no-tillage methods show potential for enhancing SOC accumulation by leveraging the protective role of aggregates.

The synergistic integration of tillage and microbial fertilizers presents a transformative short-term management strategy to enhance soil organic carbon concentration in albic soils. This approach simultaneously addresses physical constraints (through the improvement of soil aggregation and structure) and biological limitations (via the regulation of microbial metabolic regulation), thereby enhancing soil health and contributing to climate change mitigation. Previous studies have rarely compared the effects of three different tillage intensities combined with microbial fertilizer application on improving the carbon concentration capacity of albic cultivated soils. We hypothesize that high microbial fertilizer inputs with no-tillage maximizes macro-aggregate-protected SOC, whereas plough tillage with the same rate increases subsoil C but intensifies mineralization. Therefore, the objectives of this study were as follows: (1) to systematically investigate the changes in SOC concentration under the synergistic effects of different tillage practices and microbial organic fertilizer application rates; (2) to provide a feasible and preliminary short-term verification scheme for improving the carbon concentration potential of albic soils through controlled field experiments.

Materials and methods

Experimental site

A field experiment was established in the autumn of 2023 in Sanliang Village, Shulan City, Jilin Province, China (44°31' N, 126°42' E). The experiment area has a temperate continental monsoon climate, with four distinct seasons and concurrent rain and heat. During the maize growing season, the precipitation was 628.4 mm, and the mean air temperature was 17.1 °C. The annual mean effective accumulated temperature was 2,700 °C, and the frost-free period was 140 d. The tested soil is albic soil (Chinese Soil Taxonomy [2001]). The basic physicochemical properties are presented in Table 1.

Table 1. Basic properties of the experimental soil

Soil layer
(cm) BD
(g cm⁻³) pH
(1:2.5) SOC TN TP TK Sand Silt Clay

(g kg⁻¹) (%)

0–20 1.4 5.4 11.1 0.83 1.04 12.5 37.3 44.2 18.5

20–40 1.6 5.8 4.9 0.86 0.7 12.9 37.1 41.0 21.9

Tillage and fertilization practice
A two-factor split-plot design was employed, with tillage methods assigned to the main plots and organic input rate level to the subplots, resulting in 13 treatments. Each subplot comprised six rows with a 0.65 m row spacing (3.9 m × 24 m). Maize (Zea mays L.) cultivar 'Fumin 985' was planted at a density of 72,000 plants ha⁻¹, sown in early May, and harvested in early October.

Except for the control, all treatments received organic inputs annually after maize harvest, with crop stubble retained. Under no-tillage, straw and organic inputs were left on the soil surface as mulch. Under plough tillage and rotary tillage, straw and organic inputs were mechanically incorporated into the tilled layer to depths of 0–40 cm and 0–15 cm, respectively. The bio-organic fertilizer was a uniform granular product manufactured from fully composted livestock and poultry manure and inoculated with soil-derived strains of Bacillus subtilis, B. amyloliquefaciens, and Trichoderma harzianum, with viable counts ≥ 0.5 × 10⁸ CFU g⁻¹, meeting the maturity standards for use as organic fertilizer in China (NY/T 525-2021). The bio-organic fertilizer contained 423.0 g kg⁻¹ SOC, 38.3 g kg⁻¹ N, 3.1 g kg⁻¹ P₂O₅, and 8.5 g kg⁻¹ K₂O. Mineral fertilizers were applied at sowing at rates equivalent to 156 kg N ha⁻¹, 75 kg P₂O₅ ha⁻¹, and 72 kg K₂O ha⁻¹. Field management during the crop-growing season followed the local conventional high-yield cultivation practices.

Soil sampling and measurements
In October 2024, after maize harvest, soil samples were collected at two depths (0–20 and 20–40 cm). For each plot and depth, five soil cores were taken from evenly distributed positions across the plot (avoiding plot borders by ~ 0.5 m) and composited. Visible stones and plant residues (including roots and stubble fragments) were removed manually in the field, and any remaining coarse debris was further picked out in the laboratory prior to processing. The composite sample was homogenized and split into two subsamples: one subsample was gently passed through an 8-mm sieve for soil aggregate fractionation. The other subsample was air-dried at room temperature under ventilation and then sequentially sieved through 2-, 1-, and 0.25-mm meshes for the determination of soil physicochemical properties. In addition, intact soil cores were collected using a stainless-steel cutting ring (100 cm³) to determine soil bulk density and porosity.

Soil physicochemical analyses

Soil bulk density (BD) was measured by the core method of using a ring knife (volume 100 cm³). Soil porosity (SP) was determined as follows:

$ \mathrm{Soil}~\mathrm{porosity}~(\mathrm{SP})(\text{%})=\left[1-\frac{\mathrm{Soil}~\mathrm{bulk}~\text{density}\ (\mathrm{g}\; \text{cm}^{-3})}{\mathrm{Soil}~\mathrm{particle}~\text{density}\ (\mathrm{g}\; \text{cm}^{-3})}\right]\times100 $

(1)

The soil particle density is commonly adopted with a standard value of 2.65 g cm⁻³.

Soil pH was measured by using a pH meter (PB-10, Sartorius, Goettingen, Germany) with a soil : water ratio of 1:2.5 (w/v). Soil EC was measured using an electrical conductivity meter (DDS-307, Inesa Scientific Instrument Co., Ltd, Shanghai, China). SOC and TN were measured by the dry-combustion method using an elemental analyzer (Elementar Analysensysteme GmbH, Langenselbold, Germany). TP and TK were measured by NaOH-soil co-melting method using a Spectramax i3x (Molecular Devices, LLC, San Jose, CA, USA) microplate reader. Soil microbial biomass carbon (MBC) was determined using the chloroform fumigation–extraction (CFE) method following the procedure of Vance et al.^[20].

Soil aggregate analyses

Aggregate stability was determined using the wet-sieving method with a nested set of sieves (2.0, 0.25, and 0.053 mm) following Kemper and Rosenau^[21]. Briefly, air-dried soil was placed on the top sieve and pre-wetted by immersion for 5 min prior to oscillation to minimize slaking. The sieve stack was then oscillated in water (amplitude 5 cm; 60 oscillations min⁻¹) for 5 min to separate water-stable aggregates into four size classes: mega-macroaggregates (> 2 mm, MAG), large macroaggregates (0.25–2 mm, LMG), small microaggregates (0.053–0.25 mm, SMG), and the silt–clay fraction (< 0.053 mm, SC). Aggregate fractions retained on each sieve were carefully collected, oven-dried at 60 °C to constant mass, and weighed to calculate mass proportions. Dried aggregates were subsequently ground to pass a 0.25-mm sieve, and aggregate-associated carbon was determined using an elemental analyzer.

Soil enzyme analyses
Soil enzyme activities, including α-glucosidase (AG), β-glucosidase (BG), cellobiohydrolase (CBH), and endo-1,4-xylanase (XYL), were determined using commercial assay kits (Solarbio, Beijing, China) following the manufacturers' instructions. Specifically, AG, BG, CBH, and XYL were assayed with Solarbio kits BC2550, BC2565, BC4030, and BC5735, respectively. Substrate solutions, reagent concentrations, and mixing ratios were prepared according to the corresponding kit manuals. Reactions for AG, BG, and CBH were incubated at 37 °C for 30–60 min, whereas XYL was incubated at 50 °C for 1 h. After incubation, absorbance was measured using a SpectraMax i3x microplate reader (Molecular Devices, San Jose, CA, USA). Reaction products were quantified at 400 nm for AG, BG, and CBH, and at 540 nm for XYL.

Data analysis

The indices of mean weight diameter (MWD) and geometric mean diameter (GMD) were selected to describe the stability of soil aggregates, and the formulas were calculated according to the method of Kemper & Rosenau^[21]:

$ \mathrm{MWD}\;(\mathrm{mm})=\sum\nolimits_{i=1}^{n}{W}_{i}{\overline{X}}_{i} $

(2)

$ \mathrm{GMD}\;(\mathrm{mm})=\exp \left(\frac{\sum\nolimits_{i=1}^{n}{W}_{i}\ln \overline{{X}_{i}}}{\sum\nolimits_{i=1}^{n}{W}_{i}}\right) $

(3)

where, $\overline X_i $ is the mean diameter of the i-th aggregate size fraction (in this study, the mean diameter of aggregates at each particle size indicates MAG, LMG, SMG, and SC), W_i is the mass of i particle size aggregate (%).

Data processing was performed using Microsoft Excel 2021 (Microsoft Corp., Redmond, WA, USA), and figures were generated using OriginPro 2024 (OriginLab Corporation, Massachusetts, USA). Data were analyzed using a split-plot ANOVA, with tillage as the main-plot factor and microbial organic fertilizer rate as the subplot factor; block was treated as a random effect. The analysis was performed using SPSS software (version 27.0; SPSS Inc., Chicago, USA) with the least significant difference and Duncan's multiple range test applied at a significance level of p < 0.05 to examine the effects of soil physicochemical analysis, enzyme analysis, and microbial community analysis with their interactions.

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Layer (cm)	Treatment	BD (g cm⁻³)	SP (%)	EC (μS cm⁻¹)	pH	TN	TP	TK
Layer (cm)	Treatment	BD (g cm⁻³)	SP (%)	EC (μS cm⁻¹)	pH	(g kg⁻¹)
0–20	NTN	1.48 ± 0.05^a	44.03 ± 1.94^c	93.67 ± 4.07^c	5.43 ± 0.12^b	0.70 ± 0.05^c	0.96 ± 0.08^b	12.57 ± 0.31^c
	NTS	1.42 ± 0.01^ab	46.29 ± 0.44^bc	102.87 ± 2.00^b	5.61 ± 0.10^ab	0.61 ± 0.03^d	1.09 ± 0.11^ab	14.53 ± 1.10^ab
	NTOM-L	1.36 ± 0.01^bc	48.55 ± 0.44^ab	104.40 ± 4.19^ab	5.50 ± 0.22^ab	0.86 ± 0.06^b	1.25 ± 0.09^a	13.77 ± 1.00^bc
	NTOM-M	1.32 ± 0.02^c	50.19 ± 0.65^a	106.70 ± 0.95^a	5.38 ± 0.08^b	1.29 ± 0.02^a	1.33 ± 0.03^a	16.00 ± 1.30^a
	NTOM-H	1.33 ± 0.05^c	49.81 ± 1.96^a	100.53 ± 0.70^b	5.71 ± 0.03^a	0.89 ± 0.04^b	1.01 ± 0.06^b	14.10 ± 0.98^bc
	PTS	1.33 ± 0.07^ab	49.81 ± 2.61^ab	105.50 ± 1.15^a	5.47 ± 0.08^b	1.01 ± 0.02^a	1.26 ± 0.03^b	15.43 ± 0.76^a
	PTOM-L	1.37 ± 0.07^a	48.43 ± 2.73^b	105.90 ± 0.92^a	5.44 ± 0.07^b	0.99 ± 0.06^a	1.24 ± 0.03^b	13.97 ± 0.32^bc
	PTOM-M	1.24 ± 0.03^b	53.33 ± 0.95^a	106.10 ± 1.25^a	6.43 ± 0.10^a	0.73 ± 0.03^c	1.26 ± 0.01^b	14.67 ± 1.17^ab
	PTOM-H	1.33 ± 0.04^ab	49.81 ± 1.51^ab	96.43 ± 4.25^b	6.47 ± 0.02^a	0.88 ± 0.03^b	1.38 ± 0.03^a	12.87 ± 0.15^c
	RTS	1.27 ± 0.02^ab	51.95 ± 0.87^ab	96.87 ± 1.43^b	5.84 ± 0.05^a	0.77 ± 0.05^bc	1.19 ± 0.01^b	12.67 ± 0.61^a
	RTOM-L	1.21 ± 0.07^b	54.21 ± 2.46^a	98.87 ± 7.34^ab	5.82 ± 0.37^a	0.69 ± 0.05^c	1.10 ± 0.07^c	14.43 ± 1.39^a
	RTOM-M	1.35 ± 0.07^a	48.93 ± 2.78^b	104.17 ± 0.67^ab	5.55 ± 0.04^a	0.83 ± 0.06^ab	1.30 ± 0.02^a	12.70 ± 0.75^a
	RTOM-H	1.31 ± 0.08^ab	50.44 ± 3.16^ab	105.60 ± 0.80^a	5.47 ± 0.06^a	0.87 ± 0.05^a	1.09 ± 0.09^c	13.53 ± 1.15^a
20–40	NTN	1.62 ± 0.02^a	38.96 ± 0.77^b	83.77 ± 3.36^c	5.44 ± 0.03^a	0.96 ± 0.08^a	0.76 ± 0.08^a	13.80 ± 0.60^cd
	NTS	1.59 ± 0.02^a	40.07 ± 0.65^b	91.00 ± 1.35^b	5.51 ± 0.04^a	0.95 ± 0.05^a	0.71 ± 0.05^a	15.67 ± 0.76^a
	NTOM-L	1.61 ± 0.03^a	39.09 ± 1.27^b	102.37 ± 0.93^a	5.57 ± 0.02^a	0.72 ± 0.02^b	0.68 ± 0.06^a	15.27 ± 0.71^ab
	NTOM-M	1.55 ± 0.02^b	41.69 ± 0.85^a	79.87 ± 4.51^cd	5.75 ± 0.09^a	0.60 ± 0.02^c	0.40 ± 0.00^b	14.30 ± 0.53^bc
	NTOM-H	1.53 ± 0.02^b	42.45 ± 0.57^a	77.13 ± 2.55^d	5.84 ± 0.03^a	0.98 ± 0.03^a	0.36 ± 0.02^c	12.90 ± 0.44^d
	PTS	1.57 ± 0.01^a	40.60 ± 0.46^b	98.30 ± 0.87^b	5.29 ± 0.03^b	0.95 ± 0.07^b	0.54 ± 0.03^c	15.13 ± 0.59^b
	PTOM-L	1.59 ± 0.04^a	40.10 ± 1.33^b	90.27 ± 3.99^c	5.65 ± 0.32^a	0.86 ± 0.08^b	0.57 ± 0.05^bc	12.97 ± 0.57^c
	PTOM-M	1.47 ± 0.03^b	44.37 ± 0.94^a	102.27 ± 0.47^a	6.14 ± 0.02^a	0.62 ± 0.05^c	0.65 ± 0.03^b	15.20 ± 0.53^b
	PTOM-H	1.47 ± 0.03^b	44.52 ± 1.00^a	65.23 ± 3.02^d	6.02 ± 0.05^b	1.19 ± 0.11^a	0.75 ± 0.03^a	17.17 ± 0.60^a
	RTS	1.60 ± 0.03^ab	39.74 ± 0.95^ab	103.27 ± 0.46^a	5.10 ± 0.02^c	0.51 ± 0.04^a	1.26 ± 0.05^a	12.60 ± 0.79^b
	RTOM-L	1.62 ± 0.02^a	38.74 ± 0.58^c	94.67 ± 0.81^b	5.41 ± 0.02^bc	0.51 ± 0.05^a	0.67 ± 0.04^b	13.53 ± 0.25^b
	RTOM-M	1.57 ± 0.02^b	40.71 ± 0.92^b	65.97 ± 3.03^d	6.00 ± 0.18^a	0.40 ± 0.03^b	0.40 ± 0.00^c	12.60 ± 0.79^b
	RTOM-H	1.53 ± 0.02^c	42.43 ± 0.72^a	86.63 ± 6.74^c	5.60 ± 0.16^ab	0.56 ± 0.02^a	0.43 ± 0.03^c	16.27 ± 0.50^a
ANOVA (0–20)	T	**	**	*	**	**	**	**
	R	**	**	**	**	**	**	**
	T × R	ns	ns	**	**	**	**	**
ANOVA (20–40)	T	**	**	*	**	**	**	**
	R	**	**	**	**	**	**	**
	T × R	ns	ns	**	**	**	**	**
Different letters in the same column indicate significant differences between treatments at p < 0.05 according to Duncan's multiple range test. Values are means ± the standard error (n = 3). , * represent p < 0.05, p < 0.01, respectively, and 'ns' represents p > 0.05. T represents tillage methods (main plot), R represents organic input rate level (subplot), and T × R represents the interaction.

Layer (cm)	Treatment	AG	BG	CBH	XYL
Layer (cm)	Treatment	(μmol d⁻¹ g⁻¹)
0–20	NTN	22.88 ± 0.48^a	22.80 ± 1.10^c	2.03 ± 0.09^ab	47.58 ± 3.48^b
	NTS	22.04 ± 0.68^a	25.92 ± 0.63^b	1.69 ± 0.22^ab	50.90 ± 2.80^b
	NTOM-L	16.77 ± 0.60^c	36.32 ± 3.60^a	1.82 ± 0.29^ab	58.15 ± 3.42^a
	NTOM-M	17.93 ± 0.34^b	22.96 ± 1.11^c	1.46 ± 0.11^b	26.24 ± 4.13^d
	NTOM-H	22.61 ± 3.26^a	40.40 ± 4.02^a	1.58 ± 0.10^a	38.18 ± 0.72^c
	PTS	19.70 ± 0.68^b	30.40 ± 2.01^b	1.83 ± 0.02^ab	42.71 ± 1.13^a
	PTOM-L	19.45 ± 0.30^b	26.08 ± 2.57^bc	1.49 ± 0.12^ab	29.44 ± 3.54^b
	PTOM-M	19.19 ± 0.60^b	25.44 ± 2.29^c	1.44 ± 0.02^b	27.72 ± 4.49^b
	PTOM-H	21.83 ± 1.00^a	41.36 ± 4.02^a	1.98 ± 0.23^a	47.18 ± 2.10^a
	RTS	31.58 ± 3.83^a	44.96 ± 4.43^ab	1.89 ± 0.11^ab	42.89 ± 0.77^a
	RTOM-L	10.40 ± 0.45^c	38.16 ± 3.74^b	1.78 ± 0.13^ab	52.89 ± 2.84^a
	RTOM-M	30.25 ± 1.98^a	44.32 ± 1.60^a	1.90 ± 0.10^b	45.52 ± 1.57^a
	RTOM-H	19.66 ± 0.73^b	28.40 ± 2.77^c	2.00 ± 0.12^a	30.63 ± 2.34^a
20–40	NTN	12.69 ± 0.66^b	29.44 ± 2.04^b	1.20 ± 0.08^d	32.74 ± 2.55^b
	NTS	7.37 ± 0.59^c	37.36 ± 3.61^a	1.43 ± 0.14^c	29.19 ± 2.71^b
	NTOM-L	15.96 ± 0.70^a	38.48 ± 3.81^a	2.07 ± 0.13^b	23.62 ± 1.22^c
	NTOM-M	16.76 ± 0.57^a	23.52 ± 2.29^c	2.28 ± 0.31^ab	58.15 ± 3.42^a
	NTOM-H	15.53 ± 0.52^a	36.80 ± 2.98^a	2.28 ± 0.06^a	32.52 ± 4.08^b
	PTS	21.77 ± 0.55^a	30.00 ± 2.95^a	1.40 ± 0.08^b	31.53 ± 2.91^b
	PTOM-L	16.88 ± 0.69^c	21.20 ± 2.01^b	1.00 ± 0.12^c	25.32 ± 3.64^b
	PTOM-M	19.69 ± 0.40^b	23.68 ± 1.56^b	1.78 ± 0.10^a	45.52 ± 3.79^a
	PTOM-H	17.13 ± 0.52^c	20.08 ± 1.74^b	1.98 ± 0.22^a	42.31 ± 3.39^a
	RTS	13.88 ± 0.28^c	25.20 ± 2.44^c	1.48 ± 0.03^b	28.18 ± 2.72^b
	RTOM-L	14.57 ± 0.43^c	40.96 ± 4.09^a	1.31 ± 0.07^c	44.41 ± 2.88^a
	RTOM-M	19.92 ± 0.63^a	33.44 ± 3.24^b	1.44 ± 0.13^bc	27.42 ± 2.98^b
	RTOM-H	15.50 ± 0.43^b	25.52 ± 2.43^c	1.78 ± 0.02^a	27.09 ± 2.91^b
ANOVA (0–20)	T	**	**	**	**
	R	**	**	**	**
	T × R	**	**	**	**
ANOVA (20–40)	T	**	**	**	**
	R	**	**	**	**
	T × R	**	**	**	**
AG, BG, CBH, and XYL indicate the activities of α-glucosidase, β-glucosidase, cellobiohydrolase, and endo-1,4-β-xylanase, respectively. Different lowercase letters show significant differences between treatments (p < 0.05). ** represent p < 0.01, respectively.

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Tillage methods and microbial fertilizers synergistically enhance soil organic carbon concentration in albic soil

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